A wavelet analysis of oil price volatility dynamic

In this the paper we investigate the oil price volatility, by studying the causal relationships between different volatilities captured at different time scales. We first decompose the oil price volatility at various scales of resolution or frequency ranges by using wavelet analysis. We then explore the causalities between absolute returns of oil prices at different time scales. As traditional Granger causality test, designed to detect linear causality, is ineffective in uncovering certain nonlinear causal relationships, we use the nonlinear causality test introduced by Péguin-Feissolle and Teräsvirta (1999) and Péguin-Feissolle, Strikholm and Teräsvirta (2008). Our results confirm the fact that the vertical dependence is a strong stylised fact of oil returns volatility. But, the main finding consists on the presence of a feed- back effect from high frequency traders to low frequency traders. In contrast to Gençay et al. (2010), we prove that high frequency shocks could have an impact outside their boundaries and reach the long term traders.Causality, Wavelet decomposition, oil price volatility

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

open access articleThis paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models

Noncausal Bayesian Vector Autoregression

Peer reviewe

Causality between exports and economic growth in South Africa : evidence from linear and nonlinear tests

This paper investigates the dynamic causal link between exports and economic growth using both linear and nonlinear Granger causality tests. We use annual South African data on real exports and real gross domestic product from 1911-2011. The linear Granger causality result shows no evidence of significant causality between exports and GDP. The relevant VAR is unstable, which undermines our confidence in the causality result identified by the linear Granger causality test. Accordingly we turn to the nonlinear methods to evaluate Granger causality between exports and GDP. First, we use Hiemstra and Jones (1994) nonlinear Granger causality test and find a unidirectional causality from GDP to exports. However, using a more powerful and less biased nonlinear test, the Diks and Panchenko (2006) test, we find evidence of significant bi-directional causality. These results highlight the risk of misleading conclusions based on the standard linear Granger causality tests which neither accounts for structural breaks nor uncover nonlinearities in the dynamic relationship between exports and GDP.http://muse.jhu.edu/journals/jda/hb2016Economic

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Essays on expectations and the econometrics of asset pricing

The way in which market participants form expectations affects the dynamic properties of financial asset prices and therefore the appropriateness of different econometric tools used for empirical asset pricing. In addition to standard rational expectations models, this thesis studies a class of models in which boundedly rational agents may switch between various simple expectation rules. A well-known specific example features fundamentalists, who target the fundamental value of the asset, and chartists, who try to exploit recent trends in price movements. A crucial feature of these models is that not all agents have to follow the same expectation rule, but are allowed to form heterogeneous beliefs. Chapters 2 and 3 present empirical estimations of two specific heterogeneous agent models. Since the data generating processes are assumed to be nonlinear, due to the agents' switching between expectation rules, nonlinear regression models are applied. By framing the empirical results in a heterogeneous agent framework, these chapters provide an alternative view on important topics in asset pricing, such as the prevalence of excess volatility and the relation between financial markets and the macro-economy. The final two chapters deal with noncausal, or forward-looking, autoregressive models. Chapter 4 shows that US stock prices are better described by noncausal autoregressions than by their causal counterparts. This implies that agents' expectations are not revealed to an outside observer such as an econometrician observing only realized market data. Simulation results show that heterogeneous agent models are able to generate noncausal asset prices. Chapter 5 considers the estimation of a class of standard rational expectations models. It is shown that noncausality of the instrumental variables does not have an impact on the consistency of the generalized method of moments (GMM) estimator, as long as agents form rational expectations.Ei saatavill

Support for Governments and Leaders: Fractional Cointegration Analysis of Poll Evidence from the UK, 1960-2004

Revised draft, July 2005Two versions of a fractionally cointegrating vector error correction model (FVECM) are presented. In the case of regular cointegration, linear combinations of fractionally integrated variables are integrated to lower order. Generalized cointegration is defined as the case where the cointegrating variables may be fractional differences of the observed series. The concepts are applied to a model of poll data on approval of the performance of prime ministers and governments in the UK

Causality between US economic policy and equity market uncertainties : evidence from linear and nonlinear tests

This paper examines the causal relationship between economic policy uncertainty (EPU) and equity market uncertainty (EMU) in the US using linear and nonlinear Granger causality tests. We use daily data on the newly developed indexes by Baker et al. (2013a) covering 1985:01:01 to 2013:06:14. Results from the linear causality tests indicate strong bidirectional causality. We test for parameters stability, and find strong evidence of short run parameter instability, thus invalidating any conclusion from the full sample linear estimations. Therefore we turn to nonlinear tests. Using Hiemstra and Jones (1994), Diks and Panchenko (2006), and Kyrtsou and Labys (2006) symmetric test, we observe a stronger predictive power from EMU to EPU than from EPU to EMU. Using the asymmetric version of Kyrtsou and Labys (2006) test, we find no evidence of positive predictive power from EPU to EMU. However, we find strong evidence of positive predictive power from EMU to EPU and only weak evidence of negative EPU causing EMU. Performing the causality test using the Sato et al. (2007) time-varying method, we find that the causality between EPU and EMU is not constant over time but rather time-varying. Hence, we implement a sub-sample bootstrap rolling window causality tests to fully account for the existence of structural breaks. Using the intensity plots of the p-values from this, we find evidence that EPU can help predict the movements in EMU only around 1993, 2004 and, 2006. However, we find strong evidence that EMU can help predict the movements in EPU throughout the sample period barring around 1998, 2003 and 2005. Further, the analysis of total effects based on the bootstrap sum of coefficients suggests a positive and stronger causal effect from EMU to EPU but smaller and insignificant causality from EPU to EMU. The implications of these findings for both investors and policy makers are provided.http://www.journals.elsevier.com/journal-of-applied-economics2016-11-30hb201